Large-Scale Time Lapse Imaging to Monitor Neuroplasticity and Circuit Function

描述(由申请人提供)：我们建议开发一个开源软件系统，该系统将管理非常大的延时显微镜数据集的获取、存储和检索。高分辨率广域图像将由多个图像块构建，图像将以流水线方式处理，允许在图像采集进行的同时审查数据集。图像数据将以多分辨率格式存储，无论数据大小如何，都可以快速查看。图像可以是二维或三维的，并且具有一个或多个通道。该软件还将有一个与显微镜系统的接口，以便能够远程控制其操作。在建立数据集时对其进行审查并向采集系统提供反馈的能力，将使不需要研究人员在显微镜下进行的长期延时研究成为可能。Kitware将开发这一拟议的软件系统。作为Kitware创新的开源商业模式的一部分，我们将形成一个研究人员社区，他们将使用并为开放系统做出贡献。我们预计该软件将在显微镜中得到广泛应用。对自动化处理大型图像数据集的软件的需求越来越大；然而，当前的工具主要用于处理和显示适合可用RAM的图像。虽然拟议的软件将被设计用于时间推移显微镜，但它也将用于玻片成像。如果在带有电动工作台的显微镜上获得平铺的图像堆栈，该软件将能够对齐并拼接图像堆栈，从而创建大小可达数百GB的蒙太奇。该软件还将使查看和分析如此庞大的数据集变得切实可行。目前没有其他解决方案可用于此应用程序。我们建议使用该软件对明确定义的神经回路中的神经可塑性进行时间推移研究。我们能够通过使用多光子激光照射来形成基板图案来创建这些电路，从而创建不利于生长的区域。吸收多光子辐射不会对附近的神经元造成损害。因此，我们能够围绕选定的神经元设计底物，将它们与培养的其余部分隔离，并诱导它们相互作用，形成定义良好的电路。在第二阶段中，我们将建议自动化这一基板图案化过程，从而可以并行创建和监控多个电路。对于第一阶段的验证研究，我们将通过手动控制显微镜来创建电路。然后，将通过多天的电路延时成像来监测突触的重新安排。 与公共卫生相关：该项目将创建一个用于获取时间推移显微镜的开源工具包。该软件将用于研究培养中生长的神经元。这项研究最终可能应用于脊髓损伤。

DESCRIPTION (provided by applicant): We propose to develop an open-source software system that will manage acquisition, storage and retrieval of very large time-lapse microscopy datasets. High-resolution wide-field images will be constructed from multiple images tiles, and the images will be processed in a pipeline manner, allowing a dataset to be reviewed while image acquisition is ongoing. The image data will be stored in a multi-resolution format, enabling fast review irrespective of the data size. The images can be two-or three-dimensional and have one or more channels. The software will also have an interface to the microscope system in order to be able to control its operation remotely. The ability to review datasets as they are being built and provide feedback to the acquisition system will enable long-term time-lapse studies that do not require the researcher to be at the microscope. Kitware will develop this proposed software system. As part of Kitware's innovative open-source business model, we will form a community of researchers that will use and contribute to the open system. We anticipate this software will find widespread use in microscopy. There is increasing demand for software to automate handling of large image datasets; however, current tools are geared mostly toward processing and displaying images that fit in available RAM. Although the proposed software will be designed for time-lapse microscopy, it will also be useful for slide imaging. If tiled image stacks are acquired on a microscope with a motorized stage, the software will be able to align and stitch the image stacks, creating a montage that can be many hundreds of gigabytes in size. The software will also make it practical to view and analyze such a large dataset. Currently there are no alternative solutions available for this application. We propose to use this software for time-lapse studies of neuroplasticity in well defined neural circuits. We are able to create these circuits by using multiphoton laser irradiation to pattern the substrate creating regions that are inimical to growth. Absorption of multiphoton irradiation does not cause damage to nearby neurons. Thus we are able to pattern the substrate around select neurons, isolating them from the rest of the culture and inducing them to interact to form the well defined circuits. In Phase II we will propose to automate this process of substrate patterning, making it possible to create and monitor multiple circuits in parallel. For the validation study in Phase I, we will create circuits by manually controlling the microscope. Synaptic rearrangements will then be monitored by time-lapse imaging of the circuits over many days. PUBLIC HEALTH RELEVANCE: The project will create an open-source toolkit for acquiring time-lapse microscopy. The software will be used to study neurons growing in culture. The research may eventually be applied to spinal-cord injuries.